EurekAlert!

sPGGM: a sample-perturbed Gaussian graphical model for identifying pre-disease stages and signaling molecules of disease progression

(a) Disease progression can be classified into three states: the normal stage, pre-disease stage and disease stage, with the pre-disease stage representing a critical threshold just before the onset ...
Nature

Graphical Models and Covariance Matrix Estimation

Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.
EurekAlert!

Figure 1. Schematic illustration of sample-perturbed Gaussian graphical model (sPGGM) for identifying pre-disease stages. (IMAGE)

(a) Disease progression can be classified into three states: the normal stage, pre-disease stage and disease stage, with the pre-disease stage representing a critical threshold just before the onset ...
JSTOR Daily

Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property

The Andersson-Madigan-Perlman (AMP) Markov property is a recently proposed alternative Markov property (AMP) for chain graphs. In the case of continuous variables with a joint multivariate Gaussian ...
Helsinki

Janne Leppä-aho defends his PhD thesis on Methods for Learning Directed and Undirected Graphical Models

On Friday the 24th of January 2020, M.Sc. Janne Leppä-aho will defend his doctoral thesis on Methods for Learning Directed and Undirected Graphical Models. The thesis is a part of research done in the ...